Kategorie: AI Strategy

Posted in AI Strategy, Data pipelines, DataOps, Generative AI, Large Language Models

How Large Language Models are Transforming Data Operations

Large language models and generative AI are disrupting how data is done. I (Elizabeth Press from D3M Labs) spoke with Leonid Nekhymchuk (Leo), CEO and Co-Founder of Datuum.ai, about how large language models will transform data operations.  Datuum uses AI to connect data sources with target models, automate mapping, making data integration less time-consuming and less expensive. 

Posted in AI Strategy, Change management, Data as an asset, Data Leadership, Data product management, Data Products, Data science, Deutsch

Das Produktlebenszyklusmanagement im Zeitalter der intelligenten Geräte – ein Interview mit Eric JoAchim Liese

Wie managt man ein Data Science Produkt, Teil 2: Da die Geräte immer intelligenter werden, muss sich das Produktlebenszyklusmanagement weiterentwickeln, um die Daten als langfristigen Wert und Teil der Kundenbeziehung zu betrachten. Eric Joachim Liese spricht über Edge Computing und Geräteautonomie als Voraussetzung für ein gutes Kundenerlebnis. Er erklärt auch, wie traditionelle Hardware-Hersteller ihre Betriebsabläufe weiterentwickeln und Fachkräfte einstellen können, um diesen Weg erfolgreich zu beschreiten

Posted in AI Strategy, Change management, Data as an asset, Data Leadership, Data product management, Data Products, Data science

Product lifecycle management in the era of smart devices – an Interview with Eric JoAchim Liese

How to Manage the Data Science Product, Part 2: As devices get smart, product lifecycle management for hardware needs to evolve to encompass the view of data as a long-term asset and as an active, even pro-active part of the customer relationship. Eric JoAchim Liese talks about edge computing and device autonomy as being requisite to providing a good customer experience. He also explains how traditional hardware manufacturers can evolve their operations and hire in expertise to successfully navigate the journey.

Posted in AI Strategy, Data as an asset, Data Leadership, Data Products, Data science, Deutsch

Das Management des Data Science Produktes – ein Interview mit Anna Hannemann, PhD

Wie managt man ein Data Science Produkt, Teil 1: Algorithmen sind Produkte, die gemanagt werden müssen, um geschäftliche Ergebnisse zu erzielen. Anna Hannemann, PhD von Metro.digital erzählt, was sie als Pionierin im Produktmanagement für Datenwissenschaft gelernt hat. Sie spricht auch über den organisatorischen Aufbau, die Kompetenzen, die vorhanden sein müssen, und darüber, wie neue Tools das Management von Data-Science-Produkten beeinflussen.

Posted in AI Strategy, Data as an asset, Data Leadership, Data Products, Data science

Managing the data science product – an interview with Anna Hannemann, PhD

How to manage the data science product, Part 1: Algorithms are now products that need to be managed for business impact. Anna Hannemann, PhD of Metro.digital shares what she has learned as a pioneer in data science product management. She shares some key success factors for data science product development to drive monetization and growth .She also talks about organizational design, competencies that need to be in place and how new tools are impacting how data science products are managed.

Posted in AI Strategy, Data as an asset, Data product management, Data Products, Data science, Deutsch

Wie managt man ein Data Science Produkt?  – Eine Serie von D3M Labs

Die Datenwissenschaft entwickelt sich von der Forschung und Entwicklung zu Produkten – sowohl online als auch offline….

Posted in AI Strategy, Data as an asset, Data product management, Data Products

How to manage the data science product, a D3M Labs Series.

Data science is moving from R&D into products – both online and off. Managing data products requires…

Posted in AI Strategy, Business, Data as an asset, Data product management, Data strategy, Strategy

Building defensibility with Data Moats  – an interview with Raúl Berganza Gómez

Competitive advantages enable your business to be successful. Defensibility is what you need to keep that competitive advantage. Data Moats leverage data to create parts of your business that are hard for competitors to replicate. In an open source, fast-moving digital world, building a deep moat gives your business the margin and time to maintain competitiveness.

Posted in AI Strategy, AI use case, Data education, Data politics, Data Products, Data science, Education

The prevalence of AI and importance of engaging in dialogue

AI is becoming omnipresent in our lives and is shaping our world. Thus wider public involvement in determining how AI is designed and used is important for society. Understanding AI and getting involved in how it is applied and governed might seem daunting, but Varsh Anilkumar offers some ways to get involved and learn about AI.

Posted in AI Strategy, Data Leadership, Data science, Data strategy, DataOps

Beyond the algorithm, the realities of operationalizing AI – A podcast interview with Elizabeth Press

The AI mystique might be the biggest obstacle to AI adoption. The artisanal data scientist who works on an alchemy of code output the magical algorithm impedes discussion on what is needed to commercialize and scale AI solutions. AI needs to be treated like a product and an item to be manufactured and scaled on an industrial level.